Assessing water quality by remote sensing in small lakes: the case study of Monticchio lakes in southern Italy

Abstract

© iForest -Biogeosciences and Forestry IntroductionAssessing the quality of surface water in lakes, rivers and reservoirs is a key issue for environmental monitoring and management. In Europe, lakes are classified according to eco-regions (Brivio et al. 2001b), ranging from alpine to boreal, from continental to sub-arctic. Each class is based on morphological, vegetational and climatic conditions, which influence the water quality parameters such as chlorophyll concentration, turbidity, suspended sediments, and thermal characteristics. Furthermore, lakes are often subjected to sudden environmental changes caused by anthropic activities due to their multiple uses (recreational, industrial, agricultural, water supply, etc.). In order to assess lake water quality, in situ traditional methods are very accurate but have the disadvantage of being invasive, expensive and labour-intensive. In situ methods, therefore, may undersample the spatial variability required for the proper monitoring and management of lake water quality. Remote sensing provides an efficient method to enhance the in situ limnological measurements, allowing the extension of the recorded parameters to the entire lake surface.Remotely sensed data from satellites have been used for the monitoring of water quality (Lathrop & Lillesand 1986, Schiebe et al. 1992, Schneider & Mauser 1996 since the early '80s. The Thematic Mapper sensor installed on the Landsat satellite has been the most widely used sensor to monitor internal waters (Ritchie et al. 1990, Mayo et al. 1995, Kloiber et al. 2002a, Kloiber et al. 2002b, Wang et al. 2004). Thanks to its good spectral characteristics and its finer spatial resolution (30 m pixels), which allowed even small lakes to be investigated, it was particularly popular in Europe (Jaquet & Zand 1989, Baban 1993, Dekker & Peters 1993, Zilioli et al. 1994, Zilioli & Brivio 1997, Brivio et al. 2001a, Brivio et al. 2001b). Sea and ocean water monitoring also increasingly uses data from remote sensors (i.e., Aqua-MODIS, Envisat-AATSR - Strombeck et al. 2003, which guarantee repetitiveness, improving the detection of limnological parameter changes. But for lake areas with a small extension, where water monitoring is a crucial issue, the coarse resolution of such kind of sensors is not applicable.Remote sensing water quality assessment, is usually investigated using the optical bands in the region from blue to near infrared. These data are then used to explore the relation between the reflectance of water bodies and biophysical parameters such as: transparency, chlorophyll concentration (phytoplankton), and the organic and mineral suspended sediments.The first approach in the lake water quality assessment has been the analysis of chromatic coordinates in the visible part of the electromagnetic spectrum. This approach is based on the idea that an increase of radiance or reflectance at long wavelengths is an indicator of a decrease of spectral response, whereas a reduction of the spectral response towards the shorter wavelengths (b...

Landsat TM imagery and NDVI differencing to detect vegetation change: assessing natural forest expansion in Basilicata, southern Italy

“…The two images were subsequently clipped to the final study area, and radiometrically corrected using the dark object subtraction model (Chavez 1996), a widely applied methodology considered one of the best approaches for the radiometric correction in change detection analysis (Song et al 2001, Lu et al 2002, Hu et al 2004, Schroeder et al 2006, Mancino et al 2009, Wang & Xu 2010. The reliability of the radiometric correction was confirmed using an inter-calibration algorithm between the NDVI images over a test area of the study site.…”

Study on Spatiotemporal Variability of Water Quality Parameters in Florida Bay Using Remote Sensing

“…Pearson's correlation was carried out between Landsat bands, and chlorophyll and turbidity concentrations to find the most significant relationships. Previous studies [12,14,[16][17][18][19] found a very significant correlation between chlorophyll concentration and water transparency and the relationships among the visible bands. As regards to chlorophyll, the prominent scattering-absorption features of chl-a include strong absorption between 450-475 nm (blue) and at 670 nm (red), and reflectance reaches to peak at 550 nm (green) and near 700 nm (NIR).…”

“…In this study, statistical techniques were also applied to find the most significant relationships between water quality parameters and reflectance values of visible band of TM and OLI data and their combinations [10][11][12][13][14][15]. Pearson's correlation was carried out between Landsat bands, and chlorophyll and turbidity concentrations to find the most significant relationships.…”

Hyper-spectral Remote Sensing of Water Quality Parameters in Lakes: A Case Study of Hyderabad City, Telangana State, India

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